scholarly journals Methods of Decline Curve Analysis for Shale Gas Reservoirs

Energies ◽  
2018 ◽  
Vol 11 (3) ◽  
pp. 552 ◽  
Author(s):  
Lei Tan ◽  
Lihua Zuo ◽  
Binbin Wang
Keyword(s):  
Shale Gas ◽  
Curve Analysis ◽  
Gas Reservoirs ◽  
Decline Curve Analysis ◽  
Shale Gas Reservoirs ◽  
Decline Curve

Empirical methods of decline-curve analysis for shale gas reservoirs: Review, evaluation, and application

2020 ◽  
Vol 83 ◽  
pp. 103531
Author(s):  
Hong-Bin Liang ◽  
Lie-Hui Zhang ◽  
Yu-Long Zhao ◽  
Bo-Ning Zhang ◽  
Cheng Chang ◽  
...  
Keyword(s):  
Shale Gas ◽  
Curve Analysis ◽  
Empirical Methods ◽  
Gas Reservoirs ◽  
Decline Curve Analysis ◽  
Shale Gas Reservoirs ◽  
Decline Curve

scholarly journals The Evaluation and Sensitivity of Decline Curve Modelling

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2765
Author(s):  
Prinisha Manda ◽  
Diakanua Nkazi
Keyword(s):  
Shale Gas ◽  
Goodness Of Fit ◽  
Accuracy Assessment ◽  
Gas Production ◽  
Production Performance ◽  
Logistic Growth ◽  
Gas Reservoirs ◽  
Shale Gas Reservoirs ◽  
Decline Curve ◽  
Exponential Decline

The development of prediction tools for production performance and the lifespan of shale gas reservoirs has been a focus for petroleum engineers. Several decline curve models have been developed and compared with data from shale gas production. To accurately forecast the estimated ultimate recovery for shale gas reservoirs, consistent and accurate decline curve modelling is required. In this paper, the current decline curve models are evaluated using the goodness of fit as a measure of accuracy with field data. The evaluation found that there are advantages in using the current DCA models; however, they also have limitations associated with them that have to be addressed. Based on the accuracy assessment conducted on the different models, it appears that the Stretched Exponential Decline Model (SEDM) and Logistic Growth Model (LGM), followed by the Extended Exponential Decline Model (EEDM), the Power Law Exponential Model (PLE), the Doung’s Model, and lastly, the Arps Hyperbolic Decline Model, provide the best fit with production data.

A New Probabilistic Approach for Uncertainty Quantification in Well Performance of Shale Gas Reservoirs

SPE Journal ◽  
2016 ◽  
Vol 21 (06) ◽  
pp. 2038-2048 ◽  
Author(s):  
Wei Yu ◽  
Xiaosi Tan ◽  
Lihua Zuo ◽  
Jenn-Tai Liang ◽  
Hwa C. Liang ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Shale Gas ◽  
Probabilistic Approach ◽  
Model Parameters ◽  
Well Performance ◽  
Gas Reservoirs ◽  
Production Forecasting ◽  
Shale Gas Reservoirs ◽  
Decline Curve ◽  
Curve Model

Summary Over the past decade, technological advancements in horizontal drilling and multistage fracturing enable natural gas to be economically produced from tight shale formations. However, because of limited availability of the production data as well as the complex gas-transport mechanisms and fracture geometries, there still exist great uncertainties in production forecasting and reserves estimation for shale gas reservoirs. The rapid pace of shale gas development makes it important to develop a new and efficient probabilistic-based methodology for history matching, production forecasting, reserves estimates, and uncertainty quantification that are critical for the decision-making processes. In this study, we present a new probabilistic approach with the Bayesian methodology combined with Markov-chain Monte Carlo (MCMC) sampling and a fractional decline-curve (FDC) model to improve the efficiency and reliability of the uncertainty quantification in well-performance forecasting for shale gas reservoirs. The FDC model not only can effectively capture the long-tail phenomenon of shale gas-production decline curves but also can obtain a narrower range of production prediction than the classical Arps model. To predict the posterior distributions of the decline-curve model parameters, we use a more-efficient adaptive Metropolis (AM) algorithm in place of the standard Metropolis-Hasting (MH) algorithm. The AM algorithm can form the Markov chain of decline-curve model parameters efficiently by incorporating the correlation between the model parameters. With the predicted posterior distributions of the FDC model parameters generated by the AM algorithm, the uncertainty in production forecasts and estimated-ultimate-recovery (EUR) prediction can then be quantified. This work provides an efficient and robust tool that is based on a new probabilistic approach for production forecasting, reserves estimations, and uncertainty quantification for shale gas reservoirs.

Sign in / Sign up

Export Citation Format

Share Document